Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations15000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory952.4 KiB
Average record size in memory65.0 B

Variable types

Numeric8
Categorical1

Alerts

Body_Temp is highly overall correlated with Calories and 2 other fieldsHigh correlation
Calories is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Duration is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Heart_Rate is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Height is highly overall correlated with Gender and 1 other fieldsHigh correlation
Weight is highly overall correlated with Gender and 1 other fieldsHigh correlation
User_ID has unique values Unique

Reproduction

Analysis started2025-05-05 22:00:51.291414
Analysis finished2025-05-05 22:01:00.657738
Duration9.37 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

User_ID
Real number (ℝ)

Unique 

Distinct15000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14977359
Minimum10001159
Maximum19999647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:00.861384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10001159
5-th percentile10512380
Q112474191
median14997285
Q317449279
95-th percentile19472454
Maximum19999647
Range9998488
Interquartile range (IQR)4975088

Descriptive statistics

Standard deviation2872851.5
Coefficient of variation (CV)0.19181296
Kurtosis-1.1941194
Mean14977359
Median Absolute Deviation (MAD)2482539.5
Skewness0.0047883907
Sum2.2466038 × 1011
Variance8.2532755 × 1012
MonotonicityNot monotonic
2025-05-05T22:01:01.127225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11751526 1
 
< 0.1%
14733363 1
 
< 0.1%
14861698 1
 
< 0.1%
11179863 1
 
< 0.1%
16180408 1
 
< 0.1%
17771927 1
 
< 0.1%
15130815 1
 
< 0.1%
19602372 1
 
< 0.1%
19200234 1
 
< 0.1%
19807300 1
 
< 0.1%
Other values (14990) 14990
99.9%
ValueCountFrequency (%)
10001159 1
< 0.1%
10001607 1
< 0.1%
10005485 1
< 0.1%
10005630 1
< 0.1%
10006441 1
< 0.1%
10006606 1
< 0.1%
10007368 1
< 0.1%
10007686 1
< 0.1%
10008086 1
< 0.1%
10008486 1
< 0.1%
ValueCountFrequency (%)
19999647 1
< 0.1%
19999394 1
< 0.1%
19999257 1
< 0.1%
19999086 1
< 0.1%
19999044 1
< 0.1%
19998753 1
< 0.1%
19998603 1
< 0.1%
19998582 1
< 0.1%
19996063 1
< 0.1%
19994709 1
< 0.1%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
female
7553 
male
7447 

Length

Max length6
Median length6
Mean length5.0070667
Min length4

Characters and Unicode

Total characters75106
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
female 7553
50.4%
male 7447
49.6%

Length

2025-05-05T22:01:01.425419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T22:01:01.584699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 7553
50.4%
male 7447
49.6%

Most occurring characters

ValueCountFrequency (%)
e 22553
30.0%
a 15000
20.0%
m 15000
20.0%
l 15000
20.0%
f 7553
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22553
30.0%
a 15000
20.0%
m 15000
20.0%
l 15000
20.0%
f 7553
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22553
30.0%
a 15000
20.0%
m 15000
20.0%
l 15000
20.0%
f 7553
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22553
30.0%
a 15000
20.0%
m 15000
20.0%
l 15000
20.0%
f 7553
 
10.1%

Age
Real number (ℝ)

Distinct60
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.7898
Minimum20
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:01.758487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q128
median39
Q356
95-th percentile74
Maximum79
Range59
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.980264
Coefficient of variation (CV)0.39682972
Kurtosis-0.94913044
Mean42.7898
Median Absolute Deviation (MAD)13
Skewness0.4733827
Sum641847
Variance288.32937
MonotonicityNot monotonic
2025-05-05T22:01:01.965309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 512
 
3.4%
21 497
 
3.3%
22 489
 
3.3%
26 452
 
3.0%
25 435
 
2.9%
24 426
 
2.8%
23 411
 
2.7%
27 396
 
2.6%
28 390
 
2.6%
29 379
 
2.5%
Other values (50) 10613
70.8%
ValueCountFrequency (%)
20 512
3.4%
21 497
3.3%
22 489
3.3%
23 411
2.7%
24 426
2.8%
25 435
2.9%
26 452
3.0%
27 396
2.6%
28 390
2.6%
29 379
2.5%
ValueCountFrequency (%)
79 147
1.0%
78 133
0.9%
77 130
0.9%
76 139
0.9%
75 117
0.8%
74 142
0.9%
73 167
1.1%
72 137
0.9%
71 163
1.1%
70 154
1.0%

Height
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.46513
Minimum123
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:02.133440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile151
Q1164
median175
Q3185
95-th percentile197
Maximum222
Range99
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.258114
Coefficient of variation (CV)0.081724717
Kurtosis-0.51321034
Mean174.46513
Median Absolute Deviation (MAD)11
Skewness-0.0061896204
Sum2616977
Variance203.2938
MonotonicityNot monotonic
2025-05-05T22:01:02.296007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 394
 
2.6%
169 388
 
2.6%
176 383
 
2.6%
171 374
 
2.5%
177 371
 
2.5%
181 364
 
2.4%
170 360
 
2.4%
166 359
 
2.4%
182 359
 
2.4%
173 356
 
2.4%
Other values (80) 11292
75.3%
ValueCountFrequency (%)
123 1
 
< 0.1%
126 1
 
< 0.1%
127 1
 
< 0.1%
132 5
< 0.1%
133 3
 
< 0.1%
134 2
 
< 0.1%
135 7
< 0.1%
136 5
< 0.1%
137 6
< 0.1%
138 10
0.1%
ValueCountFrequency (%)
222 1
 
< 0.1%
219 1
 
< 0.1%
218 2
 
< 0.1%
217 2
 
< 0.1%
214 4
 
< 0.1%
213 6
 
< 0.1%
212 7
< 0.1%
211 10
0.1%
210 16
0.1%
209 16
0.1%

Weight
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.966867
Minimum36
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:02.735441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile53
Q163
median74
Q387
95-th percentile100
Maximum132
Range96
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.035657
Coefficient of variation (CV)0.20056403
Kurtosis-0.68205674
Mean74.966867
Median Absolute Deviation (MAD)12
Skewness0.22672531
Sum1124503
Variance226.07097
MonotonicityNot monotonic
2025-05-05T22:01:02.895991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 374
 
2.5%
67 370
 
2.5%
63 361
 
2.4%
61 360
 
2.4%
68 359
 
2.4%
60 357
 
2.4%
65 352
 
2.3%
64 352
 
2.3%
59 349
 
2.3%
62 346
 
2.3%
Other values (81) 11420
76.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
38 4
 
< 0.1%
39 2
 
< 0.1%
40 4
 
< 0.1%
41 8
 
0.1%
42 8
 
0.1%
43 11
 
0.1%
44 19
0.1%
45 32
0.2%
46 28
0.2%
ValueCountFrequency (%)
132 1
 
< 0.1%
128 1
 
< 0.1%
126 2
 
< 0.1%
124 2
 
< 0.1%
123 1
 
< 0.1%
122 1
 
< 0.1%
121 5
< 0.1%
120 1
 
< 0.1%
119 5
< 0.1%
118 2
 
< 0.1%

Duration
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5306
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:03.034035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16
Q323
95-th percentile28
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.3192033
Coefficient of variation (CV)0.53566529
Kurtosis-1.184751
Mean15.5306
Median Absolute Deviation (MAD)7
Skewness0.0047505367
Sum232959
Variance69.209144
MonotonicityNot monotonic
2025-05-05T22:01:03.152271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
26 548
 
3.7%
16 546
 
3.6%
17 543
 
3.6%
28 541
 
3.6%
8 541
 
3.6%
10 539
 
3.6%
6 533
 
3.6%
5 533
 
3.6%
11 528
 
3.5%
15 527
 
3.5%
Other values (20) 9621
64.1%
ValueCountFrequency (%)
1 230
1.5%
2 479
3.2%
3 511
3.4%
4 509
3.4%
5 533
3.6%
6 533
3.6%
7 482
3.2%
8 541
3.6%
9 517
3.4%
10 539
3.6%
ValueCountFrequency (%)
30 255
1.7%
29 483
3.2%
28 541
3.6%
27 502
3.3%
26 548
3.7%
25 526
3.5%
24 521
3.5%
23 485
3.2%
22 505
3.4%
21 506
3.4%

Heart_Rate
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.518533
Minimum67
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:03.300886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile80
Q188
median96
Q3103
95-th percentile111
Maximum128
Range61
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.5833282
Coefficient of variation (CV)0.10032952
Kurtosis-0.6442198
Mean95.518533
Median Absolute Deviation (MAD)7
Skewness-0.01070408
Sum1432778
Variance91.840179
MonotonicityNot monotonic
2025-05-05T22:01:03.468716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 564
 
3.8%
94 560
 
3.7%
101 550
 
3.7%
97 531
 
3.5%
90 526
 
3.5%
99 524
 
3.5%
98 523
 
3.5%
96 515
 
3.4%
95 511
 
3.4%
93 509
 
3.4%
Other values (49) 9687
64.6%
ValueCountFrequency (%)
67 3
 
< 0.1%
68 2
 
< 0.1%
69 5
 
< 0.1%
70 5
 
< 0.1%
71 8
 
0.1%
72 15
 
0.1%
73 28
 
0.2%
74 40
0.3%
75 54
0.4%
76 82
0.5%
ValueCountFrequency (%)
128 1
 
< 0.1%
125 2
 
< 0.1%
123 1
 
< 0.1%
122 3
 
< 0.1%
121 7
 
< 0.1%
120 7
 
< 0.1%
119 12
 
0.1%
118 21
0.1%
117 44
0.3%
116 49
0.3%

Body_Temp
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.025453
Minimum37.1
Maximum41.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:03.618698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37.1
5-th percentile38.4
Q139.6
median40.2
Q340.6
95-th percentile41
Maximum41.5
Range4.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77922992
Coefficient of variation (CV)0.01946836
Kurtosis0.51730641
Mean40.025453
Median Absolute Deviation (MAD)0.5
Skewness-0.99438242
Sum600381.8
Variance0.60719927
MonotonicityNot monotonic
2025-05-05T22:01:03.757848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
40.7 1041
 
6.9%
40.5 1040
 
6.9%
40.6 1027
 
6.8%
40.3 928
 
6.2%
40.4 915
 
6.1%
40.8 901
 
6.0%
40.2 800
 
5.3%
40.1 757
 
5.0%
40.9 660
 
4.4%
40 636
 
4.2%
Other values (35) 6295
42.0%
ValueCountFrequency (%)
37.1 1
 
< 0.1%
37.2 3
 
< 0.1%
37.3 7
 
< 0.1%
37.4 17
 
0.1%
37.5 22
 
0.1%
37.6 26
 
0.2%
37.7 61
0.4%
37.8 69
0.5%
37.9 75
0.5%
38 88
0.6%
ValueCountFrequency (%)
41.5 3
 
< 0.1%
41.4 6
 
< 0.1%
41.3 47
 
0.3%
41.2 100
 
0.7%
41.1 206
 
1.4%
41 398
 
2.7%
40.9 660
4.4%
40.8 901
6.0%
40.7 1041
6.9%
40.6 1027
6.8%

Calories
Real number (ℝ)

High correlation 

Distinct277
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.539533
Minimum1
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-05-05T22:01:03.902067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.95
Q135
median79
Q3138
95-th percentile200
Maximum314
Range313
Interquartile range (IQR)103

Descriptive statistics

Standard deviation62.456978
Coefficient of variation (CV)0.69753522
Kurtosis-0.71776263
Mean89.539533
Median Absolute Deviation (MAD)50
Skewness0.50537137
Sum1343093
Variance3900.8741
MonotonicityNot monotonic
2025-05-05T22:01:04.077679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 161
 
1.1%
13 142
 
0.9%
12 137
 
0.9%
11 137
 
0.9%
17 136
 
0.9%
20 135
 
0.9%
8 134
 
0.9%
9 131
 
0.9%
28 121
 
0.8%
4 120
 
0.8%
Other values (267) 13646
91.0%
ValueCountFrequency (%)
1 12
 
0.1%
2 46
 
0.3%
3 92
0.6%
4 120
0.8%
5 82
0.5%
6 103
0.7%
7 161
1.1%
8 134
0.9%
9 131
0.9%
10 119
0.8%
ValueCountFrequency (%)
314 1
< 0.1%
300 1
< 0.1%
295 2
< 0.1%
289 1
< 0.1%
287 1
< 0.1%
280 1
< 0.1%
276 2
< 0.1%
273 1
< 0.1%
272 1
< 0.1%
271 2
< 0.1%

Interactions

2025-05-05T22:00:58.869238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:51.778559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:52.813799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:53.910717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:54.852744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:55.791626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:56.765697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:57.953059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:58.981179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:51.912885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:52.919914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:54.032201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:54.990900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:55.912020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:56.892150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:58.056961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:59.158616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:52.056549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:53.044847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:54.148513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:55.098614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:56.029766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:57.012389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:58.186024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:59.334254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:52.198265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:53.156434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:54.264843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:55.216448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:56.155543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:57.148583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:58.292702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-05T22:00:54.625837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-05T22:00:57.842498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-05T22:00:58.761000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-05T22:01:04.184323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBody_TempCaloriesDurationGenderHeart_RateHeightUser_IDWeight
Age1.0000.0130.1350.0130.0000.0110.008-0.0020.085
Body_Temp0.0131.0000.9200.9410.0000.811-0.0010.0010.002
Calories0.1350.9201.0000.9760.1230.9160.000-0.0010.013
Duration0.0130.9410.9761.0000.0000.862-0.005-0.003-0.001
Gender0.0000.0000.1230.0001.0000.0000.7290.0000.823
Heart_Rate0.0110.8110.9160.8620.0001.000-0.000-0.0000.004
Height0.008-0.0010.000-0.0050.729-0.0001.000-0.0130.962
User_ID-0.0020.001-0.001-0.0030.000-0.000-0.0131.000-0.011
Weight0.0850.0020.013-0.0010.8230.0040.962-0.0111.000

Missing values

2025-05-05T22:01:00.270713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-05T22:01:00.460167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

User_IDGenderAgeHeightWeightDurationHeart_RateBody_TempCalories
014733363male68190.094.029.0105.040.8231.0
114861698female20166.060.014.094.040.366.0
211179863male69179.079.05.088.038.726.0
316180408female34179.071.013.0100.040.571.0
417771927female27154.058.010.081.039.835.0
515130815female36151.050.023.096.040.7123.0
619602372female33158.056.022.095.040.5112.0
711117088male41175.085.025.0100.040.7143.0
812132339male60186.094.021.097.040.4134.0
917964668female26146.051.016.090.040.272.0
User_IDGenderAgeHeightWeightDurationHeart_RateBody_TempCalories
1499019715870female22190.079.019.096.040.389.0
1499110050978male51181.087.09.091.039.644.0
1499214722670male27170.070.013.092.040.146.0
1499313584585male45179.078.011.098.039.960.0
1499418209611female48159.057.010.094.039.852.0
1499515644082female20193.086.011.092.040.445.0
1499617212577female27165.065.06.085.039.223.0
1499717271188female43159.058.016.090.040.175.0
1499818643037male78193.097.02.084.038.311.0
1499911751526male63173.079.018.092.040.598.0